import os
from pathlib import Path
import gdown
import zipfile

from tensorflow import keras
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Convolution2D, LocallyConnected2D, MaxPooling2D, Flatten, Dense, Dropout

from deepface.commons import functions

#-------------------------------------

def loadModel(url = 'https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'):
	base_model = Sequential()
	base_model.add(Convolution2D(32, (11, 11), activation='relu', name='C1', input_shape=(152, 152, 3)))
	base_model.add(MaxPooling2D(pool_size=3, strides=2, padding='same', name='M2'))
	base_model.add(Convolution2D(16, (9, 9), activation='relu', name='C3'))
	base_model.add(LocallyConnected2D(16, (9, 9), activation='relu', name='L4'))
	base_model.add(LocallyConnected2D(16, (7, 7), strides=2, activation='relu', name='L5') )
	base_model.add(LocallyConnected2D(16, (5, 5), activation='relu', name='L6'))
	base_model.add(Flatten(name='F0'))
	base_model.add(Dense(4096, activation='relu', name='F7'))
	base_model.add(Dropout(rate=0.5, name='D0'))
	base_model.add(Dense(8631, activation='softmax', name='F8'))
	
	#---------------------------------
	
	home = functions.get_deepface_home()
	
	if os.path.isfile(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5') != True:
		print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
		
		output = home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
		
		gdown.download(url, output, quiet=False)
		
		#unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
		with zipfile.ZipFile(output, 'r') as zip_ref:
			zip_ref.extractall(home+'/.deepface/weights/')
		
	base_model.load_weights(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5')	
	
	#drop F8 and D0. F7 is the representation layer.
	deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
		
	return deepface_model